This paper describes the construction of temperature and precipitation time series for climate divisions in Alaska for 1925-2015. Designed for NOAA climate monitoring applications, these new series build upon the divisional data of Bieniek et al. (2014) through the inclusion of additional observing stations, temperature bias adjustments, supplemental temperature elements, and enhanced computational techniques (i.e., climatologically aided interpolation). The new NOAA series are in general agreement with Bieniek et al. (2014), differences being attributable to the underlying methods used to compute divisional averages in each dataset. Trends in minimum temperature are significant in most divisions whereas trends in maximum temperature are generally not significant in the eastern third of the state. Likewise, the statewide rate of warming in minimum temperature (0.158°C dec-1 ) is roughly 50% larger than that of maximum temperature (0.101 °C dec-1 ). Trends in precipitation are not significant for most divisions or for the state as a whole.

Drought is a complex, least understood and one of the most expensive natural disaster. Drought impacts many sectors of environment and society. A regular question is how a current drought compares to previous droughts. Water managers, resource managers, news media and the general public want to place the event in context as they evaluate impacts, and as they attempt to plan for future events. There are many definitions of drought (meteorological, agricultural, hydrological and socioeconomic) resulting in a large number of drought metrics and indices in literature. In this study we have used Standardized Precipitation Index (SPI), a useful tool to answer these questions. SPI is a transformation of the probability of a given amount of precipitation in a set period of months. This allows for the comparison of wet/dry spells over extremely different climates and over various time scales from one month to two years (24 months).

The use of environmental temperature and its effects on plant development have been useful in determining growth stages of plants. This paper compares two Corn Growing Degree Day (GDD) calculation methods that are widely used in the US and why one is more suitable in the northern edge of the US corn-belt areas than the other. The comparison between the two accumulated GDD calculations for corn during the last 67-year period from 1948 to 2014 growing seasons for Fargo, ND, indicates that one method systematically underestimates accumulated GDDs during the days when maximum temperatures are above and minimum temperatures are below the base temperature of 50°F. Furthermore, the ratio of the difference between the two seasonal accumulations to the required accumulated GDD necessary to mature the type of corn grown in this area becomes more significant than those grown in other parts of the US where corn requires higher seasonal GDD accumulations.

Serially complete climate datasets with no missing data are necessary for a diverse group of users working in many economic sectors. In this article we describe the procedures used to create a Serially Complete Data set (SCD) for the U.S. We include the selection criterion applied to potential SCD stations, the various procedural steps and the details applied to each step. A few observations that were not previously digitized were obtained from observers official paper reports. The methods used to estimate missing data are the Spatial Regression Test and the Inverse Distance Weighting technique. Using the criterion for selecting stations we were able to include 2144 stations for the SCD that had at least 1 element (maximum/minimum temperature and/or precipitation) for a continuous period of at least 40 years. In addition, the quality control procedure assigned confidence intervals to all observations and many of the estimates. We continue to explore the options for estimating any missing data that remain after our 3 step approach and we look forward to changing the base data set form TD 3200 to GHCN.

During the winter months in the High Plains region of the United States, wind chill temperatures can reach dangerous levels for humans and animals. Knowing the frequency in which extreme wind chill temperatures occur could help forecasters know when to issue wind chill advisories and also the general public understand just how rare, or common, certain wind chill temperatures are. A climatology spanning a 37-year period was created using data from 57 stations in and around the plains portion of the High Plains region from the Integrated Surface Hourly Database at National Climatic Data Center (NCDC). These climatologies were completed for December, January, February, and the winter season as a whole, for the number of hours and days in which wind chills reach certain thresholds. Also included is an all-time low wind chill value by location. As one might expect, results show that some of the most extreme and more frequent low wind chill temperatures in the region occur in eastern North Dakota and northwestern Minnesota. In this area, several days per year can reach -40°F or lower, a temperature at which frostbite can occur within minutes. The highest number of wind chills less than or equal to -10°F occurred in January, with December and February having similar distributions of wind chill occurrence.

Trace is the amount of precipitation that is less than 0.005” (AMS, 1959). Generally, it is not a measurable amount but just enough to wet the rain gauge that it is observed in. It is a global practice that “T” (indicating “Trace”) is entered in daily precipitation records such as the National Weather Service form B-91 under the precipitation column. Although trace is not a quantitative value, it is valuable information to better assess the weather condition of the day. However, when the precipitation data are tabulated, most spreadsheet programs do not know how to deal with a character that is not a numerical value. We explain a procedure for including trace observations when evaluating precipitation behavior over a period of time or between multiple time periods. This procedure temporarily assigns a computationally insignificant value to trace observations in order to incorporate those observations into database calculations (e.g. number of precipitation days) as well as also greatly reduce the chance of ties in the precipitation rankings. Our procedure allowed us to separate individual precipitation events in perspective especially in ranking tables without changing accumulated monthly, seasonal or annual precipitation values, thus preserving the climate history of the location.

Each state in the United States of America has an institution known as the Cooperative Extension Service. These institutions, almost always associated with the Land Grant University’s tripartite mission of research, education, and extension, are in essence providers of adult education. In the case of climate science, they have been called a boundary organization which serves as a two-way intermediary between climate researchers and end-users. In order to better collaborate with the Extension Service, this investigation explored their attitudes toward, knowledge of, and willingness to use climate information and seasonal climate forecasts. A survey instrument was developed and distributed to North Carolina extension personnel in March 2009. A total of 109 responses were retrieved and analyzed. A principal finding is that extension agents need and desire to gain a better understanding of climate science and its application to agricultural practices. The respondents find seasonal climate forecasts to be useful and understand the economic value of forecast guidance. However, requested accuracy of seasonal climate forecasts is beyond the skill of current climate models. The survey results are discussed as well as their implication for future work in climate assessment programs regarding information to reduce risk in agriculture and natural resource management. In general, extension will continue to be a valued partner for the dissemination of climate tools and products by serving as an intermediary between climate scientists and end-users. This feedback loop can tailor and improve formats, content, presentation, access, and credibility of climate risk reduction decision support systems.

The winter of 2009-2010 in the Washington D.C. area will likely be remembered by many arearesidents as the snowiest in their lifetimes. A rare combination of a weak-to-moderate El Nino (wet) anda very persistent negative North Atlantic Oscillation (NAO) (cold) brought a potent combination of sub-tropical moisture and cold air together over the mid-Atlantic states including the nation’s capital, resulting in an unprecedented number of major snowstorms and totalsnowfall. The extreme winter snowfalls during the most intense periods of the winter earned the winter the nickname “Snowmageddon”.

The largest snowfallsoccurred to the north of the Capital and west and northwest of Baltimore in north-central Maryland. An analysis of historical snowfall reports for the Washington D.C. metropolitan area suggests Snowmageddon’s snowfalls were unprecedented in number and amount since historical reports are available beginning in early colonial times in the early 1600’s.

The winter and early spring of 2008-2009 brought an unusually high number of alpine dust deposition events to the Rocky Mountains of Colorado. The greatest dust accumulations were observed in the San Juan Mountains of southwestern Colorado. Significant dust accumulation was even observed along the Continental Divide in northern Colorado. The primary source for this dust has previously been identified as the Colorado Plateau. Analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric trajectory model along with satellite imagery showed that dust from the 2009 events also originated from the Colorado Plateau, especially from areas in and around northeastern Arizona that were experiencing abnormally dry conditions that spring.

The study utilized data from the BLM/USFS Remote Automated Weather Station (RAWS) network in the southwestern U.S. to identify periods of high winds corresponding to documented Colorado dust events.The RAWS database, once considered to be brief and unsuitable as a climate resource, is quickly approaching 30 years of record and provides a valuable resource for application to various climate questions. Analysis of wind data from these RAWS sites during known dust events show that a minimum threshold wind velocity exists before dust storm generation occurs, and that this threshold velocity occurs from a southwesterly direction. Threshold velocity for the daily mean speed was found to be 15 mph and 44 mph for daily maximum gusts. Wind speeds for the study region were then evaluated for the period January through April for the past 20 years in an attempt to quantify and compare both mean daily wind speed and maximum daily wind gusts on a seasonal basis. A linear regression analysis showed correlation between the Southern Oscillation Index (SOI) and the frequency of these types of high wind periods in the RAWS database, particularly during winter months. This correlation was determined to be 0.46 for daily mean wind speeds and 0.56 for maximum daily wind gusts during the months of December through April. The correlation between periods of high winds and the SOI extends through the 20 years of wind data available for these weather stations.

This paper reviews the history of calculating “consumptive water use,” later termed evapotranspiration (Et), for plants in the western U.S. The Blaney-Criddle formula for monthly and seasonal consumptive water use was first developed for New Mexico in 1942 for limited crops. The formula was based on the input of monthly mean air temperature and an empirical monthly/seasonal coefficient. Subsequent changes improved the Blaney-Criddle formula by adding more weather and crop variables. The availability of data from automated weather stations, after about 1980, that measure more weather input variables has allowed the empirical Blaney-Criddle formula to be replaced by the mechanistic standardized Penman-Monteith equation with an appropriate crop coefficient to calculate Et. The Penman-Monteith equation calculates Et under non-stressed conditions and represents the maximum Et and associated yield of the crop.

Water rights in the western U.S. have historically, and continue to be, adjudicated using variations of the Blaney-Criddle formula. The Blaney-Criddle formula, derived in farmers’ fields under water stress conditions, calculates an Et that is most closely related to average county yields during the years the measurements were taken. But the empirical relationship and the originally derived coefficients are outdated and invalid for today’s agriculture production systems and should be replaced with the Penman-Monteith equation when adjudicating water rights.